Online Video Streamer
Improve quality of video recommendations in order to increase time and engagement on site, while decreasing churn
Today’s consumer is drowning in a sea of options when it comes to streaming movies and shows online. Research shows the average user spends 21 minutes searching for something to watch. When we began working with OVS, our goal was to use Hai’s software and proprietary methodology to provide higher quality recommendations to their users with the target of decreasing both the time users spent searching and OVS’ churn rate.
Our approach
The test was narrow in terms of the data set given to our algorithm. By only being able to review the watch history of 43 OVS employees, our goal was to prove that even with such a limited data set, Hai would be able to provide higher quality recommendations than the current algorithm used by OVS. Our algorithm not only included the actual video’s chosen by employee’s, but also reviewed watch time, drop rate and engagement.
Results
After running the data, the recommendations generated from Hai were displayed alongside the OVS’ current algorithms’ recommendations. The testers job was to select which line of recommendations they preferred. We were thrilled to see that 100% of the testers chose the recommendations generated by Hai, despite the algorithm not being trained on the OVS’s data. This clearly demonstrated the advanced capability of the cross-domain, deep-learning algorithm Hai possesses, and the superior results it provides.
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